Forest Wildfire Management

Wildfire management is the study of managing, predicting, and mitigating risk of forest wildfires.

DOMAINS | forest-management | forest-wildfire | computational-sustainability

As part of the more general domain of Sustainable Forest Management, we focus on the task of Forest Fire Management which presents a number of unique challenges which push the boundaries of what is possible with existing AI/ML algorithms. These include the importance of considering:

predicting daily fire spread from historical data automatically by combining information from weather, satellite hotspot detection and other available GIS information
multiple spatial-temporal scales at all times
tradeoffs between individual and social good
paucity of supervised training data
coordinating decisions amongst a large number of agents (distributed nationwide) without frequent, extensive communication.

Relevant work on this domain has been progressing recently on theoretical models of multi-agent decision making. This research explores the intersection of theory in the field of Game Theory and Multi-Agent Reinforcement Learning (MARL), both of which study how multiple agents can adapt and optimize their decision making policies in response to changes in the world around them and actions of other decision makers.

One of the applied domains we work on frequently is Sustainable Forest Management including prediction and control of Forest Wildfires. The lab is part of an NSERC WILDFIRE STRATEGIC NETWORK (https://www.canadawildfire.org/).

Current Research in this topic:

Improved models of forest fire spread prediction from raw data using Machine Learning
Uses of Lidar in Forest Modelling

News

(unofficial) CSA Lidar Project approved (more information to come)
Lab News Dec 12, 2022 : Attended Forest Wildfire Conference on this topic.
Lab News March, 2022 : Two papers related to this topic discussed.
Lab News Dec 1, 2022 : In Fall 2022, members of the lab went to a Canadian Wildfire conference. Mark also gave a talk on research related to this topic.
Journal paper in JAIR on a new algorithm using forest wildfire as one of its motivational examples to demonstrate the difficulty of multi-agent planning in real-world domains with many agents acting at once (Ganapathi Subramanian et al., 2022).
ML for Forest Fire Journal Paper: In 2020, in collaboration with some other investigators in this strategic network, we published a review article in the Environmental Reviews journal (Jain et al., 2020) analysing the relevance of various ML algorithms to the domain and exhaustively surveying and analysing the existing research using ML for forest fire management.
NSERC/CANADA WILDFIRE STRATEGIC NETWORK: In 2019, an NSERC Strategic Network grant was confirmed in which we are involved to support computational research in to Forest Fire Management practices. This includes the AI/ML/RL focus of our research along with our colleague in computer science Prof. Kate Larson.
In 2019, we were awarded a computing grant on the topic of Wildfire management: Disaster response, climate change” from Waterloo Artificial Intelligence Institute (WAII) in affiliation with the Microsoft “AI for Social Good” program.

Our Papers on Forest Wildfire Management

Multi-Advisor-QL

Multi-Agent Advisor Q-Learning

Sriram Ganapathi Subramanian, Kate Larson, Matthew Taylor, and Mark Crowley

In International Joint Conference on Artificial Intelligence (IJCAI) : Journal Track. Macao, China. Aug, 2023.

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In the last decade, there have been significant advances in multi-agent reinforcement learn- ing (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possi- ble. However, many real-world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. An interesting question that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. In this paper, we provide a principled framework for incorporating action recommendations from online sub- optimal advisors in multi-agent settings. We describe the problem of ADvising Multiple Intelligent Reinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic game environments and present two novel Q-learning based algorithms: ADMIRAL - Decision Making (ADMIRAL-DM) and ADMIRAL - Advisor Evaluation (ADMIRAL-AE), which allow us to improve learning by appropriately incorporating advice from an advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor (ADMIRAL-AE). We analyze the algorithms theoretically and provide fixed point guarantees regarding their learning in general-sum stochastic games. Furthermore, extensive experi- ments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors.
Multi-Advisor-MARL

Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning

Sriram Ganapathi Subramanian, Matthew Taylor, Kate Larson, and Mark Crowley

In Proceedings of the 22nd International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), London, United Kingdom. Sep, 2023.

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Multi-agent reinforcement learning typically suffers from the problem of sample inefficiency, where learning suitable policies involves the use of many data samples. Learning from external demonstrators is a possible solution that mitigates this problem. However, most prior approaches in this area assume the presence of a single demonstrator. Leveraging multiple knowledge sources (i.e., advisors) with expertise in distinct aspects of the environment could substantially speed up learning in complex environments. This paper considers the problem of simultaneously learning from multiple independent advisors in multi-agent reinforcement learning. The approach leverages a two-level Q-learning architecture, and extends this framework from single-agent to multi-agent settings. We provide principled algorithms that incorporate a set of advisors by both evaluating the advisors at each state and subsequently using the advisors to guide action selection. Also, we provide theoretical convergence and sample complexity guarantees. Experimentally, we validate our approach in three different test-beds and show that our algorithms give better performances than baselines, can effectively integrate the combined expertise of different advisors, and learn to ignore bad advice.
Multi-Advisor-QL

Multi-Agent Advisor Q-Learning

Sriram Ganapathi Subramanian, Kate Larson, Matthew Taylor, and Mark Crowley

Journal of Artificial Intelligence Research (JAIR). 74, May, 2022.

Abs PDF Code URL

In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible. However, many real-world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. An interesting question that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. In this paper, we provide a principled framework for incorporating action recommendations from online sub-optimal advisors in multi-agent settings. We describe the problem of ADvising Multiple Intelligent Reinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic game environments and present two novel Q-learning based algorithms: ADMIRAL - Decision Making (ADMIRAL-DM) and ADMIRAL - Advisor Evaluation (ADMIRAL-AE), which allow us to improve learning by appropriately incorporating advice from an advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor (ADMIRAL-AE). We analyze the algorithms theoretically and provide fixed point guarantees regarding their learning in general-sum stochastic games. Furthermore, extensive experi- ments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors.
MARLEmpircal

Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments

Ken Ming Lee, Sriram Ganapathi Subramanian, and Mark Crowley

Frontiers in Artificial Intelligence. Sep, 2022.

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Independent reinforcement learning algorithms have no theoretical guarantees for finding the best policy in multi-agent settings. However, in practice, prior works have reported good performance with independent algorithms in some domains and bad performance in others. Moreover, a comprehensive study of the strengths and weaknesses of independent algorithms is lacking in the literature. In this paper, we carry out an empirical comparison of the performance of independent algorithms on seven PettingZoo environments that span the three main categories of multi-agent environments, i.e., cooperative, competitive, and mixed. For the cooperative setting, we show that independent algorithms can perform on par with multi-agent algorithms in fully-observable environments, while adding recurrence improves the learning of independent algorithms in partially-observable environments. In the competitive setting, independent algorithms can perform on par or better than multi-agent algorithms, even in more challenging environments. We also show that agents trained via independent algorithms learn to perform well individually, but fail to learn to cooperate with allies and compete with enemies in mixed environments.
PO-MFRL

Partially Observable Mean Field Reinforcement Learning

Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.

In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom. May, 2021.

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Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agents. Recent research has introduced successful methods to scale multi-agent reinforcement learning algorithms to many agent scenarios using mean field theory. Previous work in this field assumes that an agent has access to exact cumulative metrics regarding the mean field behaviour of the system, which it can then use to take its actions. In this paper, we relax this assumption and maintain a distribution to model the uncertainty regarding the mean field of the system. We consider two different settings for this problem. In the first setting, only agents in a fixed neighbourhood are visible, while in the second setting, the visibility of agents is determined at random based on distances. For each of these settings, we introduce a Q-learning based algorithm that can learn effectively. We prove that this Q-learning estimate stays very close to the Nash Q-value (under a common set of assumptions) for the first setting. We also empirically show our algorithms outperform multiple baselines in three different games in the MAgents framework, which supports large environments with many agents learning simultaneously to achieve possibly distinct goals.
MARLEmpircal

Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments

Ken Ming Lee, Sriram Ganapathi Subramanian, and Mark Crowley

In NeurIPS 2021 Deep Reinforcement Learning Workshop. Dec, 2021.

Abs arXiv

Independent reinforcement learning algorithms have no theoretical guarantees for finding the best policy in multi-agent settings. However, in practice, prior works have reported good performance with independent algorithms in some domains and bad performance in others. Moreover, a comprehensive study of the strengths and weaknesses of independent algorithms is lacking in the literature. In this paper, we carry out an empirical comparison of the performance of independent algorithms on four PettingZoo environments that span the three main categories of multi-agent environments, i.e., cooperative, competitive, and mixed. We show that in fully-observable environments, independent algorithms can perform on par with multi-agent algorithms in cooperative and competitive settings. For the mixed environments, we show that agents trained via independent algorithms learn to perform well individually, but fail to learn to cooperate with allies and compete with enemies. We also show that adding recurrence improves the learning of independent algorithms in cooperative partially observable environments.
WildfireMLRev

A review of machine learning applications in wildfire science and management

Piyush Jain, Sean CP Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, and Mike D Flannigan.

Environmental Reviews. 28, (3). Canadian Science Publishing, Jul, 2020.

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Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date, and then review the use of ML in wildfire science as broadly categorized into six problem domains, including: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, we identified 298 relevant publications, where the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods, such as deep learning, requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high quality, and freely available wildfire data for use by practitioners of ML methods.
A Complementary Approach to Improve WildFire Prediction Systems.

Sriram Ganapathi Subramanian, and Mark Crowley

In Neural Information Processing Systems (AI for social good workshop). NeurIPS. 2018.

PDF URL
MCTS+A3C

Combining MCTS and A3C for prediction of spatially spreading processes in forest wildfire settings

Sriram Ganapathi Subramanian, and Mark Crowley

In Canadian Conference on Artificial Intelligence. Toronto, Ontario, Canada. 2018.

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In recent years, Deep Reinforcement Learning (RL) algorithms have shown super-human performance in a variety Atari and classic board games like chess and GO. Research into applications of RL in other domains with spatial considerations like environmental planning are still in their nascent stages. In this paper, we introduce a novel combination of Monte-Carlo Tree Search (MCTS) and A3C algorithms on an online simulator of a wildfire, on a pair of forest fires in Northern Alberta (Fort McMurray and Richardson fires) and on historical Saskatchewan fires previously compared by others to a physics-based simulator. We conduct several experiments to predict fire spread for several days before and after the given spatial information of fire spread and ignition points. Our results show that the advancements in Deep RL applications in the gaming world have advantages in spatially spreading real-world problems like forest fires. \textcopyright Springer International Publishing AG, part of Springer Nature 2018.
Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images

Sriram Ganapathi Subramanian, and Mark Crowley

Frontiers in ICT. 5, (6). Frontiers, Apr, 2018.

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Machine learning algorithms have increased tremendously in power in recent years but have yet to be fully utilized in many ecology and sustainable resource management domains such as wildlife reserve design, forest fire management and invasive species spread. One thing these domains have in common is that they contain dynamics that can be characterized as a Spatially Spreading Process (SSP) which requires many parameters to be set precisely to model the dynamics, spread rates and directional biases of the elements which are spreading. We present related work in Artificial Intelligence and Machine Learning for SSP sustainability domains including forest wildfire prediction. We then introduce a novel approach for learning in SSP domains using Reinforcement Learning (RL) where fire is the agent at any cell in the landscape and the set of actions the fire can take from a location at any point in time includes spreading North, South, East, West or not spreading. This approach inverts the usual RL setup since the dynamics of the corresponding Markov Decision Process (MDP) is a known function for immediate wildfire spread. Meanwhile, we learn an agent policy for a predictive model of the dynamics of a complex spatially-spreading process. Rewards are provided for correctly classifying which cells are on fire or not compared to satellite and other related data. We examine the behaviour of five RL algorithms on this problem: Value Iteration, Policy Iteration, Q-Learning, Monte Carlo Tree Search and Asynchronous Advantage Actor-Critic (A3C). We compare to a Gaussian process based supervised learning approach and discuss the relation of our approach to manually constructed, state-of-the-art methods from forest wildfire modelling. We also discuss the relation of our approach to manually constructed, state-of-the-art methods from forest wildfire modelling. We validate our approach with satellite image data of two massive wildfire events in Northern Alberta, Canada, the Fort McMurray fire of 2016 and the Richardson fire of 2011. The results show that we can learn predictive, agent-based policies as models of spatial dynamics using RL on readily available satellite images that other methods and have many additional advantages in terms of generalizability and interpretability.
Learning Forest Wildfire Dynamics from Satellite Images Using Reinforcement Learning

Sriram Ganapathi Subramanian, and Mark Crowley

In Conference on Reinforcement Learning and Decision Making. Ann Arbor, MI, USA.. 2017.
Allowing a wildfire to burn: Estimating the effect on future fire suppression costs

Rachel M. Houtman, Claire A. Montgomery, Aaron R. Gagnon, David E. Calkin, Thomas G. Dietterich, Sean McGregor, and Mark Crowley

International Journal of Wildland Fire. 22, (7). 2013.

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Where a legacy of aggressive wildland fire suppression has left forests in need of fuel reduction, allowing wildland fire to burn may provide fuel treatment benefits, thereby reducing suppression costs from subsequent fires. The least-cost-plus-net-value-change model of wildland fire economics includes benefits of wildfire in a framework for evaluating suppression options. In this study, we estimated one component of that benefit – the expected present value of the reduction in suppression costs for subsequent fires arising from the fuel treatment effect of a current fire. To that end, we employed Monte Carlo methods to generate a set of scenarios for subsequent fire ignition and weather events, which are referred to as sample paths, for a study area in central Oregon. We simulated fire on the landscape over a 100-year time horizon using existing models of fire behaviour, vegetation and fuels development, and suppression effectiveness, and we estimated suppression costs using an existing suppression cost model. Our estimates suggest that the potential cost savings may be substantial. Further research is needed to estimate the full least-cost-plus-net-value-change model. This line of research will extend the set of tools available for developing wildfire management plans for forested landscapes.